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ZeroDivision

What a metric returns when its denominator is zero.

public enum ZeroDivision { Zero, One, NaN, Throw }

Members — Zero returns 0.0, which is scikit-learn's default value. One returns 1.0, its zero_division=1. NaN returns double.NaN, its zero_division=np.nan. Throw raises UndefinedMetricException and has no scikit-learn equivalent.

Example — one sample of class 1, and a model that never predicts it.

using Lodestar.Metrics;

int[] yTrue = [0, 0, 1];
int[] yPred = [0, 0, 0];

double asZero = Precision.Score(yTrue, yPred);                                  // => 0
double asOne = Precision.Score(yTrue, yPred, zeroDivision: ZeroDivision.One);   // => 1

Remarks — the choice is about what an unanswerable question should look like downstream, and there is no universally right answer, which is why it is a parameter. Zero is the safe default and the one that keeps parity, at the cost of reading in a report as a real, terrible score. One is the optimistic reading — "we were never wrong about a class we never predicted" — and is what scikit-learn's zero_division=1 exists for. NaN is the honest one when the number is about to be averaged: a NaN propagates and is visible, where a 0.0 quietly pulls a macro average down by 1/k.

The default is not the same everywhere, and that is worth checking rather than assuming. The precision family defaults to Zero; CohenKappa.Score and the regression side's R2 default to NaN, because that is the value scikit-learn returns for their undefined cases. Each entry states its own.

The trap is One in an average. It does not merely hide the problem, it inverts it: a class nothing was predicted into contributes the best possible score to a macro average, so adding classes your model ignores raises the number.

Applies to — net10.0, netstandard2.0.

See also — UndefinedMetricException, Precision.Score, Recall.Score, the regression page, the Python equivalence table.

Members

Member	What it does